A Hardware Acceleration System Oriented to LSTM Network Model

Abstract

The invention discloses a hardware acceleration system oriented to an LSTM network model, and belongs to the technical field of deep learning hardware acceleration. The invention discloses a hardware acceleration system oriented to a deep learning long short-term memory (LSTM) network model, which includes a network reasoning calculation core and a network data storage core. As the computing accelerator of the LSTM network model, the network reasoning computing core deploys computing units according to the network model to realize the computing acceleration of convolution operations, matrix point multiplication, matrix addition, activation functions and other computing units; the network data storage core serves as the data of the LSTM network model The cache and interaction controller deploys the on-chip cache unit according to the network model to realize the data interaction link between the computing core and the off-chip memory. The invention improves the calculation parallelism of the LSTM network model, reduces the processing delay, reduces the memory access time, and improves the memory access efficiency.

Description

technical field [0001] The invention belongs to the field of deep learning hardware acceleration, and more specifically relates to a hardware acceleration system oriented to an LSTM network model. Background technique [0002] Long Short-Term Memory (LSTM), as a variant of deep learning Recurrent Neural Network (RNN), is widely used in sequence model processing tasks such as speech recognition, natural language processing, and image compression. LSTM effectively solves the problem of gradient explosion and gradient disappearance in the RNN training process by introducing a gating mechanism and a state value for storing long-term and short-term historical information, and relatively greatly increases its computational complexity and space complexity. Its intensive calculation and memory access limit its application on the embedded hardware platform with limited resources. Therefore, it is a very meaningful research to design and accelerate the optimization of the LSTM model f...

AI Technical Summary

Problems solved by technology

Although this method realizes the acceleration of the LSTM model system, it mainly optimizes the bottleneck of storage resource consumption, and the optimization effect at the computing level is average.

[0006] In summary, although the research on LSTM model acceleration tasks for hardware platforms is of great significance, the research results in this area are still relatively scarce, and an LSTM network acceleration design with good versatility and excellent acceleration effects is urgently needed

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